Last Year Morten Jerven called into question the quality of statistics produced by (African) developing countries. In his musings on the political fallout from his publication, Professor Jerven summed up the situation as “governance by ignorance.” It also is clear that many stakeholders have a similar view but are reluctant to say so publicly for fear of the ‘anti-neo-colonialist’ backlash that Prof Jerven experienced.
Although I am slightly shocked that a statistic with as high a profile as GDP is so poorly computed, I really shouldn’t be surprised. Some of the government estimates for forest cover that the FAO collates each year in its annual Forest Resources Assessment are known to be extremely shaky; in some cases they are reportedly based on data years out of date, in others on little more than expert guesses. As with GDP, again many stakeholders are aware of this, and, in the case of donors, have money to throw at the problem.
Unfortunately this fits with a wider pattern in conservation: that we are getting better and better at identifying and measuring the biodiversity we are losing, but not much better at halting those losses. This is not to say that new knowledge is a bad thing; it is a vanishingly rare occurrence when an addition to our total body of knowledge does not increase the public good. But, as many researchers are all too aware, new knowledge can easily be misinterpreted or, worse, abused.
A paper last year from Gardner et al. (A framework for integrating biodiversity concerns into national REDD+ programmes) showed how biodiversity could be incorporated into REDD+ planning, and illustrated this with the map reproduced below that overlays biodiversity and carbon values for the case of Tanzania.
As I understand it, the map is a fair reflection of the current state of knowledge, and no-one should infer any particular agenda on the part of the paper authors. As a simplified guide to national decision making it looks eminently useful.
The trouble is, from what I gathered on a recent visit to the country, that this map may not be a good guide as to where are the best opportunities for effective REDD+ projects. The map highlights the Eastern Arc mountains as an area of “high opportunity (strong positive correlation in carbon and biodiversity values)” for REDD+ intervention. Most of these forests are, in theory, already protected in national parks and forest reserves, but which are threatened by encroachment and illegal resource extraction. A long developed strategy of Joint Forest Management (JFM) is intended to help resolve this, by giving local communities a share of forest revenue, except that this has been held up by the failure of the Tanzanian Government to agree a benefit sharing mechanism for JFM. I.e. potential REDD+ project developers would be advised to steer well clear of JFM in the Eastern Arc Mountains until the benefit sharing mechanism has been agreed and tested in practice.
Such policy issues cannot easily be represented on maps, and I would not expect an overlay to do so. However, theoretical exercises like this can be dangerous in how they may give policy makers and donors the illusion of agency: invest money in the sweet spots and best return on investment will be achieved. This omits the critical step that one needs credible potential solutions before investing money in actual projects. However, the reality of national planning in developing countries, often donor supported, and which this paper purports to assist, is that high level decisions may too easily be made without all the necessary information. Maps such as these are therefore potentially dangerous in implying a higher level of decision-directing knowledge than in fact exists.
(A second criticism is that such mapping exercises can sometimes almost imply a terra nullius – no-one’s land – attitude in which the wishes of existing inhabitants and forest users are irrelevant.)
In conclusion I would question the wisdom of a top down planning approach at all. Rather than prioritising problems I suggest we might be better off prioritising solutions. In which case the value of such maps are rather less than might be first supposed.
Bottom Up Thinking 
Posted by Shaz Jameson on October 6, 2013 at 10:32 pm
Great post. I’m researching flood management in Southern India and I’ve come up against very much the same thing. A few, biophysical variables are singled out by engineering “experts” and mapped with pretty colours. The map is spat out and then somewhat paraded as legitimate.
Harsh, I know. It’s easy to say that ‘oh it depends on what you want the map for’, but as you say, it very easily slips into false confidence.
The only thing I would add is that not only does such a dodgy map give a false sense of confidence, it very explicitly prioritises certain knowledge types. In my field the typical example is expert engineers rather than communities with lived experiences.
Posted by Map-a-doodle-do | Bottom Up Thinking on October 21, 2013 at 9:19 am
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